I’ve been commissioned by the head of a research department at INRA to propose design artefacts that help scientists take new perspectives on their work. The aim is to discuss social, cultural, economical, juridical, ethical implications of these researches.

Here are the results of the questionnaires. More details on our thoughts on these results to come soon in this blog post.

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I’ve been commissioned by the head of a research department at INRA to propose design artefacts that help scientists take new perspectives on their work. The aim is to discuss social, cultural, economical, juridical, ethical implications of these researches.

The main topic is: “Challenges and Opportunities of the BigData for Predictive Biology”. In clear, how does massive statistics turns diagnostics into prognostics – based as much on DNA sequencing as on more general users datas. The targets are scientists and practicians from the fields of medicine and agronomy (plant and animals). The deliverable is a series of scenarios materialising relevant problematics/controversies in the field, presented as a solo poster session aside a one day conference on November 28th 2014.

We proposed 4 posters covering 4 main topics related to BigData and Predictive Biology. Thanks to a questionnaire we collected participants’ impressions. Questionnaires, informal interviews, audio and video recordings of the situation were used mainly to identify the next public where to show this itinerant project.
Team = Charles Chalas (discovery phase), the Sociable Media group (development phase), Jeremie Lasnier (defining and delivery phase), Fred, Juste & Annie (delivery phase).

Subtlety is a key component. The audience of the conference had voluntarily access to a framed portion of informations, under the shape of these posters. The subject is so vast and the sub-problematics are so numerous, that four scenarios – containing specific trails to specific issues – were the best way to let scientists engage with our approach. Indeed, a poster combines one general topic and clues to sub-problematics. Therefore participants would recognise one or another sub-issue depending on their own sensitivity.

Why hiding these informations (the articulation of problematics between and within each scenarios, plus the path that lead to the generation to these ideas)? Because they were not generated by (or in close collaboration with) scientists. We believe our scenarios to still highlight relevant problematics for biologists. However, if we would have provide those hidden details, we believe participants would pay less attention to the consequences of these scenarios into society and the probable link to their research. Indeed, when we shown them in private, one participant sought to correct our “designer point of view on a biology topic” in terms of precision and scientific validity.

About these details, we could organise the sub-problematics suggested by these scenarios into several dimensions, like a “design-space”. However, in order to respect the medium used during the conference and to reveal the suggested problematics, we just updated the posters, as you will see now.

These four scenarios explore the consequences of three main topics: DNA sequencing; DNA selection; DNA reprograming. They were generated using home made “cards” specifically made for this project, they decompose a list of main problematics and concepts associated to Big data & biology (find more in the previous blog-post). Another key for reading these scenarios is this text. I wrote it to summarise my understanding of the subject – prior to the scenarios making phase:

I’ve been commissioned by the head of a research department at INRA to propose design artefacts that help scientists take new perspectives on their work. The aim is to discuss social, cultural, economical, juridical, ethical implications of these researches.

After collecting an enormous amount of data I went through a processing phase where some colleagues and I tried to define more tightly the topic (we could call this reframing the brief, or building a ‘design-space’). Then we began developing ideas. Here are pictures of the key steps of this process.

There are three main problems in this project: a double complexity (span and depth) and the inter-dependency of all these elements as a complex system.
• The topic cover a large span from biology (plants, animals, humans) to computer science (software, hardware), linked by statistics & prediction;
• For each sub-topic the depth of actors involved is comparable to russian puppets, including many levels of sub-problematics (e.g. from society to family, to a human, to organs, to tissues, to cells, to molecules, to atoms. We could do the same in the bio-informatics sub-topic);
• In each of these layers every actors are inter-related, each layers is also inter-related, these topics are therefore like a giant ecosystems composed of sub-ecosystems.

The mission here is to identify relevant problematics in the domain. Different ways of orgainsing this complexity were therefore used to grasp a sense of understanding of the issues involved. For instance, Latour‘s actor network theory was a source of inspiration when tying to list the series of actors surrounding a tumor. I therefore tried to draw “the path” that takes a tumor (among actors) when a patient gets sick / gets a treatment. We could do the same with actors working around a piece of hardware on a computer dedicated at deep-learning. We could also find more theoretical inspiration in complex system studies.

The span of complexity of this topic, from biology to computer science and statistics

Actors in contact with a tumor + circulation of genomic data needed for tumor analysisDifferent layers of ecosystems in a living entity – like fractalsSummary of key problematics of the topic, key associated concepts and list of existing practices correlated to these concepts (First (successful) draft (but 6th attempt))Clean version of this summary taking the shape of “cards” to play with in a design session / workshopCards summarising the general topic: Big data and predictive biology

I therefore summarise the topic with the following text in order to introduce a team workshop.

One of the design steps we took was to list existing practices potentially associated to big data & biology (thanks to the cards shown previously). Here is a non exhaustive list of them. (Thanks Emeline, Ferdinand, the Sociable Media team for your help)

I’ve been commissioned by the head of a research department at INRA (Institut National de Recherche en Agronomie) to provide design artefacts that help scientists take new perspectives on their work. From the client side, the aim is also to discuss social, cultural, economical, juridical, ethical (etc.) implications of these researches and to spark discussions. From our side, the aim is to allow this project to circulate along different publics (mediation spaces, audiences, purposes).
Our hypothesis is that the efficiency and impact of design fiction’s generated discussions would be greater if its public would be constructed bottom-up, based on participants recommendations. At least, it is a first way to formulate it.

The main topic is: “Challenges and Opportunities of the BigData for Predictive Biology”. In clear, how does massive statistics turns diagnostics into prognostics – based an two kinds of datas: DNA sequencing and people & things’ data in general.
The targets are scientists and practicians from the fields of medicine and agronomy (plant and animals). Mathematicians, bio-informaticians, statisticians, etc. might be part of the target later in the project.
The deliverable is a series of scenarios materialising relevant problematics/controversies in the field, presented as a “solo poster session” aside Les journées One Health Îles-de-France‘s one day conference on November 28th 2014.

Find more details bellow (As the client is french, lot of the content is not in English. For more details contact maxmollon[at]gmail[dot]com).

In order to reframe the brief I first went through a “discovering” phase where I explored different kind of information: basic info given by the client, documents overviews on the topic; vulgarisation of the topic; very precise details (e.g. scientific papers); and general inspiration. Find below some preview of them.

Most of the precise and useful content were collected during interviews I made in Paris and Lausanne (e.g. A team working on the intestinal microbiot; the director of a medicine university; a researcher and veterinary; a computer scientist; a machine learning PhD student; a geneticist PhD student; a responsible of french cooperatives for meat production; “l’eprouvette” public platform for mediating science issues in Lausanne; and a lot of more people informally.

Real Prediction Machines (Auger-Loizeau with Alan Murray and Subramanian Ramamoorthy)
(see project description below)
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New project to be exhibited at the Crafts Council London this september:
Modern day fortune-telling is far-removed from the mystical readings of natural and celestial phenomena it once was.
Today it is all about data.
Emerging from research into artificial intelligence and cybernetics post-WW2 and increasingly made possible by an exponential growth of available data via digital networks and sophisticated sensors, prediction is fast becoming a life-changing science. The institutions of finance, such as trading, insurance and gambling are already inexorably linked to prediction algorithms. More recently we have seen a shift into online retail – Amazon.com for example has just gained a patent for ‘anticipatory shipping,’ this initiates the delivery process before the customer has clicked the purchase button. All of these systems routinely use data provided by people going about their normal (and sometimes private) business.
This project explores how data and algorithms could be reclaimed for personal use – individuals can select a specific event to be predicted such as a domestic argument; the likelihood of ones own death or the chances of a meteor strike. A service provider then determines the necessary data/sensory inputs required for an algorithm to predict the event. The output from the algorithm controls a visual display on the prediction machine, informing the owner if the chosen event is approaching, receding or impending.

This research project starts on the corner of a coffee table with my PhD supervisor. It starts with an intuition, here formulated into hypothesis: “The efficiency and impact of design fiction’s generated discussions would be greater if its public would be constructed ‘bottom-up’, based on participants/audience recommendations.”